What is accuracy? Is it a good model?
Accuracy is a metric for evaluating classification models. It is calculated by dividing the number of correct predictions by the number of total predictions.
Accuracy is not a good performance metric when there is imbalance in the dataset. For example, in binary classification with 95% of A class and 5% of B class, a constant prediction of A class would have an accuracy of 95%. In case of imbalance dataset, we need to choose Precision, recall, or F1 Score depending on the problem we are trying to solve.